This dissertation is concerned with configuring stochastic local search for combinatorial optimization problems by means of new automatic experimental procedures. The configuration and selection of stochastic local search is determined by an univocally specified computer-based evaluation of various variants of stochastic local search by means of racing. Depending on the amount of effort that is needed to generate a solution, the quality of the solution and the run time that is required to obtain the solution are taken into account in a specific way: If the effort required to generate the solutions is more or less equal for all candidates, then the solution quality is the main criterion for selection among the candidates; if there are big differences, then the run time is explicitly taken account of as well; and in cases where the search algorithms can generate a series of solutions with increasing quality, the quality of every new solution is taken into account together with the associated run time in order to select among the candidates. The application of these selection procedures is illustrated in this dissertation with the application of construction heuristics, the application of iterative improvement and the application of iterated local search to twelve distinct scheduling problems. Each time, the final result is a search algorithm whose performance is on a par with the state-of-the-art for the problem domain. In addition, new insights could be obtained on the influence of search algorithm components on algorithm performance in relation to the structure of the optimization problems that were investigated.